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The AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.
Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.
The App provides hundreds of quizzes and practice exam about:
– Machine Learning Operation on AWS
– Data Engineering
– Computer Vision,
– Exploratory Data Analysis,
– ML implementation & Operations
– Machine Learning Basics Questions and Answers
– Machine Learning Advanced Questions and Answers
– Countdown timer
– Machine Learning Cheat Sheets
– Machine Learning Interview Questions and Answers
– Machine Learning Latest News
The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.
Domain 1: Data Engineering
Create data repositories for machine learning.
Identify data sources (e.g., content and location, primary sources such as user data)
Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)
Identify and implement a data ingestion solution.
Data job styles/types (batch load, streaming)
Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.
Domain 2: Exploratory Data Analysis
Sanitize and prepare data for modeling.
Perform feature engineering.
Analyze and visualize data for machine learning.
Domain 3: Modeling
Frame business problems as machine learning problems.
Select the appropriate model(s) for a given machine learning problem.
Train machine learning models.
Perform hyperparameter optimization.
Evaluate machine learning models.
Domain 4: Machine Learning Implementation and Operations
Build machine learning solutions for performance, availability, scalability, resiliency, and fault
Recommend and implement the appropriate machine learning services and features for a given
Apply basic AWS security practices to machine learning solutions.
Deploy and operationalize machine learning solutions.
Machine Learning Services covered:
AWS Deep Learning AMIs (DLAMI)
Amazon Fraud Detector
Other Services and topics covered are:
AWS ML application services
Language relevant to ML (for example, Python, Java, Scala, R, SQL)
Notebooks and integrated development environments (IDEs),
S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena
Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift
Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.
Note and disclaimer: We are not affiliated with Microsoft or Azure or Google or Amazon. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.
- [R] NVIDIA Merlin Recommender Systems + Transformer texts embeddingsby /u/Great_Produce_2800 (Machine Learning) on September 25, 2022 at 3:28 am
Hey guys, has anyone seen an example of NVTabular feature engineering workflow including text feature processing with a custom Transformer model like BERT, RoBERTa, etc? They seem to focus on tabular data, but sometimes the core signal for a recommender system is just the title of an item.The other option is forming a feature vector manually but the model is defined from the data schema. Would be grateful for help! submitted by /u/Great_Produce_2800 [link] [comments]
- [P] Enhancing local detail and cohesion by mosaicing with stable diffusion Gradio Web UIby /u/Illustrious_Row_9971 (Machine Learning) on September 25, 2022 at 3:07 am
submitted by /u/Illustrious_Row_9971 [link] [comments]
- [P] NVIDIA A6000s from $0.42/hby /u/jonathan-lei (Machine Learning) on September 25, 2022 at 1:18 am
Hello! I’m Jonathan from TensorDock. We've been working on a marketplace for GPU virtual machines. Essentially, independent hosts from around the world run our software on their bare metal servers, and then clients can provision virtual machines. These are virtual machines, not Docker containers — those are also in the pipeline for Q1 next year. Given our lower costs, I think you guys would find this as a nice alternative to other clouds for you to train your ML models. 2080 Ti: From $0.12/hr 3080: From $0.17/hr 3090: From $0.27/hr A6000s: From $0.42/hr Available machines: https://marketplace.tensordock.com/order_list Product page: https://www.tensordock.com/product-marketplace All our current hosts are vetted. Many run from their own basements/offices, though we also have a few data center machines. Keep in mind these servers aren't ours — they're hosted by independent hosts. We're really looking to target early-stage startups, researchers, and students whose #1 priority is cost, not security. If you're interested in the cheapest servers, hopefully this interests you! If you need better security, we also have a secure Core Cloud product, which I showcased here a few months ago 🙂 If you have extra GPUs lying around, you can also apply to become a host here and make 2-3x what mining used to make when it still existed 😂 Happy to give some starting credits if you email me at jonathan [at] tensordock.com. This product is still very much in development, so expect a few bugs here and there — if you could email those to us, we'll implement those very quickly. I'm here to answer your questions, so post them below! submitted by /u/jonathan-lei [link] [comments]
- [D] State of the art techniques to create good understandings of embedding spacesby /u/vanilla-acc (Machine Learning) on September 24, 2022 at 11:51 pm
I have a model that turns image => embeddings. I want to have a very good understanding of my embedding space. What are techniques to understand the embedding space better? I know there's basics, like PCA, and t-SNE, but are there new research papers that talk about how to do this better? Maybe it's possible to learn a model that better understands the embedding space? submitted by /u/vanilla-acc [link] [comments]
submitted by /u/SuperVisualApp [link] [comments]
- [R] State-of-the-art voice cloningby /u/the_javi_himself (Machine Learning) on September 24, 2022 at 6:21 pm
I have tens of hours of recordings with my voice and I want to train (from scratch or transfer learning) a TTS model with my voice. I tried to figure out the what the state-of-the-art at this particular task is, but I don't find any benchmarks. Do you know what the sota for this task is in Sept 2022? submitted by /u/the_javi_himself [link] [comments]
- [D] How to learn a boolean outcome on an n-dimensional numerical train- and test-dataset?by /u/simonbaars (Machine Learning) on September 24, 2022 at 5:52 pm
I'm quite new to machine learning, so I may be in the wrong place or ask a stupid question. I'm trying to create a simple ML prediction model, but I have no idea where to start or what to Google. Whatever I search on Google usually results in something like image/video predictions or learning numerical outcomes. I have a training dataset like the following: PRICE, AMOUNT_ORDERS, ..., IS_FRAUD 40.45, 15 , ..., 0 12.43, 2 , ..., 0 98.09, 1 , ..., 1 ... , ... , ..., ... It contains some dimensions of numerical data (like price, number of orders, etc.) and a column indication of whether it was a fraudulent transaction (0 or 1). I have a test dataset with the same columns, except for the last one (the boolean IS_FRAUD). Based on learnings from my training set, I would like to predict IS_FRAUD. Since this is a relatively straightforward ML problem, I imagine there should be a library that I just feel the n-dimensional numerical training set, and it automatically constructs a model, with no further effort needed. But I have no idea how to approach something like that. Is there a library, Python/Java or otherwise, that supports such a feature? Or would more advanced training methods be required? submitted by /u/simonbaars [link] [comments]
- [D] Is a GPT-J successor in the works?by /u/lorepieri (Machine Learning) on September 24, 2022 at 5:48 pm
Is a new open source model GPT-3-style in the works? A sort of GPT-J successor, with more parameters. If nothing is know, how likely is for the model to be released in the next year? The success of Stable Diffusion suggests that open source biz models may makes sense, so ... pretty likely? What do you think? submitted by /u/lorepieri [link] [comments]
- [D] Best ways to perform feature learning on time series databy /u/Zalkwalker (Machine Learning) on September 24, 2022 at 5:21 pm
The tutorials for this topic are really few . Would love for the community to share some github repo links and some useful scripts. Been stuck on this for a while , auto-encoders are too time consuming for feature learning. Trying to explore some other options that beats or comes close to auto-encoders in-terms of finding non-linear relationships in the data. Any form of help is deeply appreciated . submitted by /u/Zalkwalker [link] [comments]
- [D] How to generate structured parameters from a spectrogram?by /u/Golitan11 (Machine Learning) on September 24, 2022 at 4:49 pm
Say I have an algorithm that accepts as input structured parameters of the following format, generates an audio clip and then a 512x512 spectrogram out of it: [ param1 = numeric_value, param2 = numeric_value, ..., param100 = numeric_value ] How can I do the opposite? That is, provide a 512x512 spectrogram and get a set of random candidate parameter values that would yield a similar but random spectrogram if fed into the algorithm? In terms of text-to-image models, I see this as the opposite problem. Instead of using a prompt to generate a random matching image, I would like to use an image to obtain a random matching "prompt" that's not natural language (i.e. structured and numeric). Regarding the algorithm, we can assume that the amount of changes in a resulting spectrogram is proportional to the amount of changes in the parameter values. That is, close to no parameter changes will yield a very similar spectrogram than the previous, making training somewhat possible. The algorithm is also deterministic and will always produce the same output for a given input. Is this possible? GANs seemed to be a nice architecture for this knowing that I can generate as many "real" training data as I want using the algorithm. The generator would generate a random list of structured parameters from a spectrogram, whereas the discriminator would check whether the parameter list is real or fake (i.e. coming from my training set or the generator). In practice though, I'm not sure how I would implement any of this knowing that GANs are usually not used that way (they usually produce images, not the other way around). There might also be a better architecture for this use case that I'm not aware of (e.g. latent space encoder). Any help would be appreciated. Thanks! submitted by /u/Golitan11 [link] [comments]
- [P][R] Whisper, a general-purpose speech recognition model by OpenAI with Gradio Demoby /u/Illustrious_Row_9971 (Machine Learning) on September 24, 2022 at 4:47 pm
submitted by /u/Illustrious_Row_9971 [link] [comments]
- [P] Speed Up Stable Diffusion by ~50% Using Flash Attentionby /u/hnipun (Machine Learning) on September 24, 2022 at 2:31 pm
We got close to 50% speedup on A6000 by replacing most of cross attention operations in the U-Net with flash attention Annotated Implementation: https://nn.labml.ai/diffusion/stable_diffusion/model/unet_attention.html#section-45 Github: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/stable_diffusion/model/unet_attention.py#L192 We used this to speed up our stable diffusion playground: promptart.labml.ai submitted by /u/hnipun [link] [comments]
- [D] Neural network for multivariate time series with labelsby /u/adenml (Machine Learning) on September 24, 2022 at 1:11 pm
Hello, let's say I have a dataset where, at each day I have some sort of event with some details (not only numeric data). For example: 2 Jan 2022 - Event: Payment, Channel: POS, Industry: 4939 (recreational and sports equipment rental activities), Amount: 200, Currency: EUR 3 Jan 2022 - Event: Received Email, Campaign: 3RDW52UDW3, Purpose: Customer Care, Reason: Premium Client, Email Code: 42353tf4 7 Jan 2022 - Event: Money Transfer, Channel: Online, Type: "Salary", Amount: 3000, Currency: EUR 14 Jan 2022 - Event: Disable Notifications, Channel: Mobile app Is there a neural network model where I can input somehow most of the data? I've discovered TST (time series transformer https://timeseriesai.github.io/tsai/models.TST.html) which kinda does what I need, but it removed the text information that I might need. I have to embed beforehand the labels such as "Customer Care", "Premium Client", "POS" into some numeric values. My intuition is that if a model learns the embeddings for the labels, it will understand How to order them in terms of impact, importance etc How to associate them with events. ("POS" cannot appear in an event of type "Received Email") However, I did not find any multivariate time series dealing with many (couple of hundreds) different labels associated with the events. Do you have any ideas of such a model or how it should be implemented? P.S: I know I can split transactional data from campaign data etc and have multiple simpler models, but let's assume I can't / don't want that, I want to have a single model for this as I have enough data to learn from and enough processing power to train a big model submitted by /u/adenml [link] [comments]
- [D] Is a 3 years bsc hons “computer science” with a 1 year Msc “artificial intelligence” enough to be called and also find a job of a ML/AI engineer ?by /u/coldcoldcoldcoldasic (Machine Learning) on September 24, 2022 at 11:22 am
Here’s the masters program if anyone was curious https://www.mitropolitiko.edu.gr/en/programmes-of-study/faculty-of-computing/msc-artificial-intelligence/ Thanks submitted by /u/coldcoldcoldcoldasic [link] [comments]
- [R] META researchers generate realistic renders from unseen views of any human captured from a single-view RGB-D cameraby /u/SpatialComputing (Machine Learning) on September 24, 2022 at 11:02 am
submitted by /u/SpatialComputing [link] [comments]
- [R] Mega: Moving Average Equipped Gated Attention. By using LSTM-style gates, Mega outperforms Transformer and S4 over Long Range Area, NMT, ImageNet, Wikitext-103 and raw speech classification.by /u/hardmaru (Machine Learning) on September 24, 2022 at 9:48 am
submitted by /u/hardmaru [link] [comments]
- [R] GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagesby /u/utopiah (Machine Learning) on September 24, 2022 at 8:19 am
submitted by /u/utopiah [link] [comments]
- [D] Who plans and makes preliminary designs for any new ML project in your organization?by /u/aadoop6 (Machine Learning) on September 24, 2022 at 7:49 am
View Poll submitted by /u/aadoop6 [link] [comments]
- [D] Prediction of concurrent, not future, steps of multivariate time series after simulated perturbation?by /u/desmin88 (Machine Learning) on September 24, 2022 at 5:32 am
Most MTS methods are focused on predicting the future steps of MTS, or classifying MTS. I've been unable to find anything where concurrent steps are predicted. For example, artificially perturb a ground truth MTS by zeroing out a single signal and predicting the rest of the signals change from ground truth in the MTS. Has anyone seen anything like this? submitted by /u/desmin88 [link] [comments]
- [D] If I have to choose between a RTX 3090 24GB and a RTX 4090 RTX for Stable Diffusion, MidJourney and other AI art engines that exist possibly in the future... is the RTX 4090 going to be THAT much greater and worth buying?by /u/cleverestx (Machine Learning) on September 23, 2022 at 8:04 pm
Any pros and cons? (besides price, because I know the 3090 is much cheaper). I play games also, but honestly both these cards would blow the socks of my current card. (GTX 1650), I'm mostly buying for the AI/ML generation stuff. Thank you submitted by /u/cleverestx [link] [comments]
- [P] Core ML Study Groupby /u/Remarkable-Brother (Machine Learning) on September 23, 2022 at 6:36 pm
Hello everyone! I'm looking to create a tight, kickass, dedicated group of 3-4 people who are studying ML/CV and misc. Humans work well in motivated tribes: it's easy to feed off each other's energy. If nothing else, when done right, we learn just to feel accepted. 📃 About Me: 2021 CS Undergrad, self-taught ML/CV through online courses. Hustled for FT/research work in ML/CV for ~2y. Currently work as an MLE. Looking back, I faked-it-till-I-made-it and it's all superficial. Average Math aptitude, average ML knowledge, helluva imposter syndrome. 🎯 Target Topics: Anything a Data Scientist/ML Engineer/Applied Scientist may want: Books/courses on Probability, Stats. We study, do the Math, and teach other. Yes, we drill down to the most basic topics such as Maximum Likelihood Estimation. I have suggestions. Theoretical ML, CV basics, DL architectures. Yes, we learn about architectures but we also implement basic backprop. Interviewing isn't a cakewalk. Practice, practice, practice. Implement PapersWithCode, old Kaggle competitions with solutions. 👨🎓 Target Audience: Someone like me. You currently work or study in the field, you "know" theoretical ML, CV/NLP, you prepare for interviews but with the nagging thought that it's just superficial cramming. You like asking dumb questions. Nothing furthers learning more than a group of people asking dumb questions that you're otherwise scared to ask elsewhere. You wanna make big bucks. No beginners, please. No geniuses either. Just plain Joes. Interested folks can comment below or DM me! Any suggestions or thoughts are always welcome. Please note, I am super serious about this. submitted by /u/Remarkable-Brother [link] [comments]
- [N] 1.5M Prize for Arguing for or Against AI Risk and AI Timelinesby /u/respectableacademic (Machine Learning) on September 23, 2022 at 6:03 pm
The Future Fund is a philanthropy planning to spend money on making AI safe and beneficial, but "we think it’s really possible that we’re wrong!" To encourage people to debate the issues and improve their understanding, they're "announcing prizes from $15k-$1.5M to change our minds" on when AI becomes advanced or whether it will pose risks. There will also be an independent panel of generalists judging submissions. Enter your arguments for or against AI risk or AI timelines by December 23! https://ftxfuturefund.org/announcing-the-future-funds-ai-worldview-prize/ submitted by /u/respectableacademic [link] [comments]
- [D] What is the common/best practice for sharing codebase in data science team?by /u/Wakeme-Uplater (Machine Learning) on September 23, 2022 at 4:54 pm
Hello, I wonder what is the common/best practice for sharing codebase in data science team? To elaborate, I work in a data science team on a similar theme Naturally, we use Jupyter notebook that is running on GCP, each person spawn their own instance But because they were similar theme are code duplications between each person/project Moreover, if we want to iterate on a project, the new version is just a copy of an old notebook version. So the file management become a nightmare when we also want to maintain the old version, let alone when we want to deploy them (that's MLOps job) In micro-service paradigm, mashing everything together is not a good idea (monolith), and each project should have their own bounded context. But because this is data science, I'm not sure how much it can be apply here I've read that for large tech companies like Google, Microsoft, and Meta use Mono-Repo to improve code cohesion. But doing so would make versioning nearly impractical An alternative method would be Multi-Repo, This way has the benefit of forking, if ones wish to modify the code, but this can eventually break the codebase synergy I've thought about aggregate the shared code into a single codebase, and compile them into whl for simpler project dependency, but it might be a hassle if they want to modify the codebase (monkey patch?) I've asked several of my friends, but the practice seems to be wildly difference or non-existence at all. So I am not sure what is the common/best practice. Thanks in advance submitted by /u/Wakeme-Uplater [link] [comments]
- Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom modelsby Goktug Cinar (AWS Machine Learning Blog) on September 23, 2022 at 4:54 pm
This post is co-written by Goktug Cinar, Michael Binder, and Adrian Horvath from Bosch Center for Artificial Intelligence (BCAI). Revenue forecasting is a challenging yet crucial task for strategic business decisions and fiscal planning in most organizations. Often, revenue forecasting is manually performed by financial analysts and is both time consuming and subjective. Such manual
- [D] Is there theory as to why in GANs, training the generator and discriminator intermittently proves ineffective?by /u/ETerribleT (Machine Learning) on September 23, 2022 at 1:33 pm
It seems it is a common intuition everyone has while building GANs, to pause training the generator to let the discriminator catch up and vice versa, hoping for convergence. But from what I've read, the consensus is that this is ineffective, which is disappointing. Is there any theoretical understanding of why something that seems this "obvious" doesn't work? Many of the sources I'm reading are from the earlier days of GANs, and I don't know if this understanding has changed in recent years. I'm fairly new to this topic so please excuse my ignorance. submitted by /u/ETerribleT [link] [comments]
- [N] Google releases TensorStore for High-Performance, Scalable Array Storageby /u/That_Violinist_18 (Machine Learning) on September 23, 2022 at 1:17 am
Blog post: https://ai.googleblog.com/2022/09/tensorstore-for-high-performance.html GitHub: https://github.com/google/tensorstore Documentation: https://google.github.io/tensorstore/ Today we are introducing TensorStore, an open-source C++ and Python software library designed for storage and manipulation of n-dimensional data that: Provides a uniform API for reading and writing multiple array formats, including zarr and N5. Natively supports multiple storage systems, including Google Cloud Storage, local and network filesystems, HTTP servers, and in-memory storage. Supports read/writeback caching and transactions, with strong atomicity, isolation, consistency, and durability (ACID) guarantees. Supports safe, efficient access from multiple processes and machines via optimistic concurrency. Offers an asynchronous API to enable high-throughput access even to high-latency remote storage. Provides advanced, fully composable indexing operations and virtual views. submitted by /u/That_Violinist_18 [link] [comments]
- Detect population variance of endangered species using Amazon Rekognitionby Jyothi Goudar (AWS Machine Learning Blog) on September 22, 2022 at 9:25 pm
Our planet faces a global extinction crisis. UN Report shows a staggering number of more than a million species feared to be on the path of extinction. The most common reasons for extinction include loss of habitat, poaching, and invasive species. Several wildlife conservation foundations, research scientists, volunteers, and anti-poaching rangers have been working tirelessly
- How Amazon Search reduced ML inference costs by 85% with AWS Inferentiaby Joao Moura (AWS Machine Learning Blog) on September 22, 2022 at 6:12 pm
Amazon’s product search engine indexes billions of products, serves hundreds of millions of customers worldwide, and is one of the most heavily used services in the world. The Amazon Search team develops machine learning (ML) technology that powers the Amazon.com search engine and helps customers search effortlessly. To deliver a great customer experience and operate
- Amazon Comprehend Targeted Sentiment adds synchronous supportby Raj Pathak (AWS Machine Learning Blog) on September 21, 2022 at 9:27 pm
Earlier this year, Amazon Comprehend, a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text, launched the Targeted Sentiment feature. With Targeted Sentiment, you can identify groups of mentions (co-reference groups) corresponding to a single real-world entity or attribute, provide the sentiment associated with each entity mention, and offer
- Run machine learning enablement events at scale using AWS DeepRacer multi-user account modeby Marius Cealera (AWS Machine Learning Blog) on September 21, 2022 at 4:19 pm
This post was co-written by Marius Cealera, Senior Partner Solutions Architect at AWS, Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, Cloud Architect at Accenture. Machine learning (ML) is a high-stakes business priority, with companies spending $306 billion on ML applications in the past 3 years. According to Accenture, companies that scale ML across
- Enable intelligent decision-making with Amazon SageMaker Canvas and Amazon QuickSightby Aleksandr Patrushev (AWS Machine Learning Blog) on September 21, 2022 at 4:15 pm
Every company, regardless of its size, wants to deliver the best products and services to its customers. To achieve this, companies want to understand industry trends and customer behavior, and optimize internal processes and data analyses on a routine basis. This is a crucial component of a company’s success. A very prominent part of the
- Amazon SageMaker Autopilot is up to eight times faster with new ensemble training mode powered by AutoGluonby Janisha Anand (AWS Machine Learning Blog) on September 21, 2022 at 3:04 pm
Amazon SageMaker Autopilot has added a new training mode that supports model ensembling powered by AutoGluon. Ensemble training mode in Autopilot trains several base models and combines their predictions using model stacking. For datasets less than 100 MB, ensemble training mode builds machine learning (ML) models with high accuracy quickly—up to eight times faster than
- Configure a custom Amazon S3 query output location and data retention policy for Amazon Athena data sources in Amazon SageMaker Data Wranglerby Meenakshisundaram Thandavarayan (AWS Machine Learning Blog) on September 20, 2022 at 10:41 pm
Amazon SageMaker Data Wrangler reduces the time that it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of
- Use RStudio on Amazon SageMaker to create regulatory submissions for the life sciences industryby Rohit Banga (AWS Machine Learning Blog) on September 20, 2022 at 5:46 pm
Pharmaceutical companies seeking approval from regulatory agencies such as the US Food & Drug Administration (FDA) or Japanese Pharmaceuticals and Medical Devices Agency (PMDA) to sell their drugs on the market must submit evidence to prove that their drug is safe and effective for its intended use. A team of physicians, statisticians, chemists, pharmacologists, and
- Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabularby Xin Huang (AWS Machine Learning Blog) on September 20, 2022 at 5:39 pm
Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. These algorithms and models can be used for both supervised and unsupervised learning. They can process various types of input data, including tabular,
- Parallel data processing with RStudio on Amazon SageMakerby Raj Pathak (AWS Machine Learning Blog) on September 19, 2022 at 4:39 pm
Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE, and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML)
- Discover insights from Zendesk with Amazon Kendra intelligent searchby Rajesh Kumar Ravi (AWS Machine Learning Blog) on September 16, 2022 at 7:02 pm
Customer relationship management (CRM) is a critical tool that organizations maintain to manage customer interactions and build business relationships. Zendesk is a CRM tool that makes it easy for customers and businesses to keep in sync. Zendesk captures a wealth of customer data, such as support tickets created and updated by customers and service agents,
- Amazon SageMaker Automatic Model Tuning now provides up to three times faster hyperparameter tuning with Hyperbandby Doug Mbaya (AWS Machine Learning Blog) on September 16, 2022 at 4:42 pm
Amazon SageMaker Automatic Model Tuning introduces Hyperband, a multi-fidelity technique to tune hyperparameters as a faster and more efficient way to find an optimal model. In this post, we show how automatic model tuning with Hyperband can provide faster hyperparameter tuning—up to three times as fast. The benefits of Hyperband Hyperband presents two advantages over
- Read webpages and highlight content using Amazon Pollyby Mike Havey (AWS Machine Learning Blog) on September 16, 2022 at 3:23 pm
In this post, we demonstrate how to use Amazon Polly—a leading cloud service that converts text into lifelike speech—to read the content of a webpage and highlight the content as it’s being read. Adding audio playback to a webpage improves the accessibility and visitor experience of the page. Audio-enhanced content is more impactful and memorable,
- Use Amazon SageMaker Data Wrangler for data preparation and Studio Labs to learn and experiment with MLby Rajakumar Sampathkumar (AWS Machine Learning Blog) on September 15, 2022 at 4:14 pm
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment based on open-source JupyterLab for anyone to learn and experiment with ML using AWS ML compute resources. It’s based on the same architecture and user interface as Amazon SageMaker Studio, but with a subset of Studio capabilities. When you begin working on ML
- Announcing Visual Conversation Builder for Amazon Lexby Thomas Rindfuss (AWS Machine Learning Blog) on September 14, 2022 at 8:38 pm
Amazon Lex is a service for building conversational interfaces using voice and text. Amazon Lex provides high-quality speech recognition and language understanding capabilities. With Amazon Lex, you can add sophisticated, natural language bots to new and existing applications. Amazon Lex reduces multi-platform development efforts, allowing you to easily publish your speech or text chatbots to
- Get better insight from reviews using Amazon Comprehendby Rushdi Shams (AWS Machine Learning Blog) on September 13, 2022 at 4:19 pm
“85% of buyers trust online reviews as much as a personal recommendation” – Gartner Consumers are increasingly engaging with businesses through digital surfaces and multiple touchpoints. Statistics show that the majority of shoppers use reviews to determine what products to buy and which services to use. As per Spiegel Research Centre, the purchase likelihood for
- Prepare data at scale in Amazon SageMaker Studio using serverless AWS Glue interactive sessionsby Sean Morgan (AWS Machine Learning Blog) on September 13, 2022 at 4:01 pm
Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and
- Save the date: Join AWS at NVIDIA GTC, September 19–22by Jeremy Singh (AWS Machine Learning Blog) on September 12, 2022 at 6:41 pm
Register free for NVIDIA GTC to learn from experts on how AI and the evolution of the 3D internet are profoundly impacting industries—and society as a whole. We have prepared several AWS sessions to give you guidance on how to use AWS services powered by NVIDIA technology to meet your goals. Amazon Elastic Compute Cloud
- How Medidata used Amazon SageMaker asynchronous inference to accelerate ML inference predictions up to 30 times fasterby Rajnish Jain (AWS Machine Learning Blog) on September 12, 2022 at 6:36 pm
This post is co-written with Rajnish Jain, Priyanka Kulkarni and Daniel Johnson from Medidata. Medidata is leading the digital transformation of life sciences, creating hope for millions of patients. Medidata helps generate the evidence and insights to help pharmaceutical, biotech, medical devices, and diagnostics companies as well as academic researchers with accelerating value, minimizing risk,
- [D] Simple Questions Threadby /u/AutoModerator (Machine Learning) on September 11, 2022 at 3:00 pm
Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]
- [D] Machine Learning - WAYR (What Are You Reading) - Week 140by /u/ML_WAYR_bot (Machine Learning) on June 19, 2022 at 9:49 pm
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Previous weeks : 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 Week 1 Week 11 Week 21 Week 31 Week 41 Week 51 Week 61 Week 71 Week 81 Week 91 Week 101 Week 111 Week 121 Week 131 Week 2 Week 12 Week 22 Week 32 Week 42 Week 52 Week 62 Week 72 Week 82 Week 92 Week 102 Week 112 Week 122 Week 132 Week 3 Week 13 Week 23 Week 33 Week 43 Week 53 Week 63 Week 73 Week 83 Week 93 Week 103 Week 113 Week 123 Week 133 Week 4 Week 14 Week 24 Week 34 Week 44 Week 54 Week 64 Week 74 Week 84 Week 94 Week 104 Week 114 Week 124 Week 134 Week 5 Week 15 Week 25 Week 35 Week 45 Week 55 Week 65 Week 75 Week 85 Week 95 Week 105 Week 115 Week 125 Week 135 Week 6 Week 16 Week 26 Week 36 Week 46 Week 56 Week 66 Week 76 Week 86 Week 96 Week 106 Week 116 Week 126 Week 136 Week 7 Week 17 Week 27 Week 37 Week 47 Week 57 Week 67 Week 77 Week 87 Week 97 Week 107 Week 117 Week 127 Week 137 Week 8 Week 18 Week 28 Week 38 Week 48 Week 58 Week 68 Week 78 Week 88 Week 98 Week 108 Week 118 Week 128 Week 138 Week 9 Week 19 Week 29 Week 39 Week 49 Week 59 Week 69 Week 79 Week 89 Week 99 Week 109 Week 119 Week 129 Week 139 Week 10 Week 20 Week 30 Week 40 Week 50 Week 60 Week 70 Week 80 Week 90 Week 100 Week 110 Week 120 Week 130 Most upvoted papers two weeks ago: /u/tetatetata: Why Philosophers Should Care About Computational Complexity Besides that, there are no rules, have fun. submitted by /u/ML_WAYR_bot [link] [comments]
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